Pattern recognition involves classification of data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations (vectors), defining points in a multidimensional space. A pattern recognition system may include a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; and a classification or description scheme that performs the actual function of classifying or describing observations, relying on the extracted features.
The classification or description scheme is usually based on the availability of a set of patterns that have already been classified or described. This set of patterns is termed the training set and the resulting learning strategy is characterized as supervised learning. Learning can also be unsupervised, in the sense that the system is not given an a priori labeling of patterns, instead it establishes the classes itself based on statistical regularities of the patterns.
A wide range of algorithms can be applied for pattern recognition, from very simple Bayesian classifiers to neural networks. An artificial neural network (ANN), often just called a “neural network” (NN), is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing based on a connectionist approach to computation. An ANN can be an adaptive system that changes its structure based on external or internal information that flows through the network. Artificial neural networks can be used to model complex relationships between inputs and outputs or to find patterns in data. For many years, academia and industry have been researching pattern recognition based on artificial neural networks. However, this research has yielded few practical real-world applications.
Typical applications for pattern recognition are automatic speech recognition, classification of text into several categories (e.g. spam/non-spam email messages), the automatic recognition of handwritten postal codes on postal envelopes, or the automatic recognition of images of human faces. The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems.
Biological information, e.g., electrocardiogram (ECG), electroencephalogram (EEG), electromyography (EMG) and multielectrode array data, is a new area in which pattern recognition has useful applications, including automatic monitoring, diagnosis, and prognosis in healthcare.
Programmable logic devices (PLDs) are a type of digital integrated circuit that can be programmed to perform specified logic functions. One type of PLD, the field programmable gate array (FPGA), typically includes an array of configurable logic blocks (CLBS) surrounded by a ring of programmable input/output blocks (IOBs). Some FPGAs also include additional logic blocks with special purposes (Digital Signal Processing (DSP) blocks, Random Access Memory (RAM) blocks, Phase Lock Loops (PLL), and so forth). FPGA logic blocks typically include programmable logic elements such as lookup tables (LUTs), flip flops, memory elements, multiplexers, and so forth. The LUTs are typically implemented as RAM arrays in which values are stored during configuration (i.e., programming) of the FPGA. The flip-flops, multiplexers, and other components may also be programmed by writing configuration data to configuration memory cells included in the logic block. For example, the configuration data bits can enable or disable elements, alter the aspect ratios of memory arrays, select latch or flip-flop functionality for a memory element, and so forth. The configuration data bits can also select interconnection between the logic elements in various ways within a logic block by programmably selecting multiplexers inserted in the interconnect paths within CLB and between CLBs and IOBs.